# Skill Optimizer > Implements intelligent skill optimizer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/skill-optimizer` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/skill-optimizer` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/skill-optimizer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/skill-optimizer --- # Skill Optimizer Orchestrates intelligent skill selection and execution for skill optimizer workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def optimize_skill_selection( task_context: Dict[str, Any], skill_registry: List[Dict[str, Any]], historical_metrics: Dict[str, List[float]] ) -> Dict[str, Any]: """Compute multi-factor scores for available skills and select the optimal route. Implements the scoring pipeline: 1. Text similarity matching against skill triggers 2. Historical success rate weighting 3. Dependency health validation 4. Confidence decay application """ if not task_context.get("intent"): raise ValueError("Task context missing required 'intent' field") scored_candidates = [] for skill in skill_registry: # Factor 1: Trigger similarity trigger_match = _compute_semantic_similarity(task_context["intent"], skill["triggers"]) # Factor 2: Historical performance skill_name = skill["name"] history = historical_metrics.get(skill_name, []) avg_success = sum(history) / len(history) if history else 0.5 # Factor 3: Dependency health deps_healthy = all( _check_dependency_health(dep) for dep in skill.get("dependencies", []) ) if not deps_healthy: continue # Composite scoring with configurable weights base_score = ( 0.4 * trigger_match + 0.4 * avg_success + 0.2 * skill.get("availability_weight", 1.0) ) # Apply confidence decay based on recent failures recent_failures = skill.get("recent_failures", 0) decay_factor = max(0.1, 1.0 - (recent_failures * 0.15)) final_score = base_score * decay_factor scored_candidates.append({ "skill": skill, "score": final_score, "factors": { "similarity": trigger_match, "historical": avg_success, "decay_applied": decay_factor } }) if not scored_candidates: return {"selected": None, "reason": "No viable candidates after dependency filtering"} # Sort and select top candidate scored_candidates.sort(key=lambda x: x["score"], reverse=True) best = scored_candidates[0] # Update confidence metrics for learning loop _record_selection_metric(best["skill"]["name"], best["score"]) return { "selected_skill": best["skill"], "confidence_score": best["score"], "selection_factors": best["factors"], "audit_id": _generate_audit_trace() } ``` ### Pattern 2: Execution with Fallback ```python def resolve_fallback_chain( failed_skill: Dict[str, Any], execution_context: Dict[str, Any], fallback_registry: List[Dict[str, Any]] ) -> Dict[str, Any]: """Execute fallback chain with adaptive parameter adjustment and audit logging. Implements the 2-level minimum fallback requirement: 1. Parameter retry with adjusted constraints 2. Alternative skill routing from related-skills 3. Human escalation if confidence drops below threshold """ audit_log = [] current_skill = failed_skill attempt_count = 0 while attempt_count < 3: attempt_count += 1 try: # Adjust parameters for retry (Law 1: Early Exit on invalid state) adjusted_context = _adapt_parameters_for_retry( execution_context, failed_skill.get("failure_reason", "unknown") ) # Execute with strict timeout and validation result = _execute_skill_with_validation(current_skill, adjusted_context) # Success path: record outcome and update confidence _update_confidence_score(current_skill["name"], success=True) audit_log.append({"attempt": attempt_count, "status": "success", "skill": current_skill["name"]}) return {"status": "resolved", "result": result, "audit": audit_log} except TransientTimeoutError: audit_log.append({"attempt": attempt_count, "status": "retry", "skill": current_skill["name"]}) continue except CriticalDependencyError as e: # Fallback to alternative skill from registry alt_skill = _find_related_skill(current_skill, fallback_registry) if alt_skill: audit_log.append({"attempt": attempt_count, "status": "fallback_triggered", "from": current_skill["name"], "to": alt_skill["name"]}) current_skill = alt_skill continue else: # Escalate to human operator return { "status": "escalated", "reason": f"All fallbacks exhausted for {failed_skill['name']}", "context": execution_context, "audit": audit_log } # Final fail state return { "status": "failed", "error": "Maximum fallback attempts reached", "audit": audit_log, "requires_human_review": True } ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | |---|---| | `skill-improver` | The improvement counterpart — optimizer measures performance, improver acts on the findings | | `skill-observability` | Provides telemetry data that skill optimizers use to identify underperforming patterns | --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content. - [Hyperparameter Optimization (Scikit-Learn)](https://scikit-learn.org/stable/modules/grid_search.html) — Scikit-learn's documentation on systematic parameter optimization techniques - [Bayesian Optimization for HPO (BoTorch)](https://botorch.org/) — Botorch framework for Bayesian optimization in machine learning model tuning - [AutoML and Neural Architecture Search](https://arxiv.org/abs/1905.01979) — Research paper on automated machine learning and architecture search techniques - [Resource Optimization in ML Systems (Google ML Blog)](https://ai.googleblog.com/2023/08/optimizing-ml-models-for-production.html) — Google's blog post on optimizing ML models for production deployment - [Optimization Algorithms Survey (Bengio, 2017)](https://www.foundationsbook.com/) — Yoshua Bengio's comprehensive survey of optimization algorithms for deep learning systems